Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations181136
Missing cells4262237
Missing cells (%)84.0%
Duplicate rows5878
Duplicate rows (%)3.2%
Total size in memory38.7 MiB
Average record size in memory224.0 B

Variable types

Numeric1
Categorical10
Text9
Unsupported8

Alerts

Carrier has constant value "Tata Docomo"Constant
Unnamed: 20 has constant value "0.0"Constant
Unnamed: 22 has constant value "user"Constant
Unnamed: 23 has constant value "0.9"Constant
Unnamed: 24 has constant value "0.0"Constant
Dataset has 5878 (3.2%) duplicate rowsDuplicates
Number is highly overall correlated with Unnamed: 19High correlation
Unnamed: 10 is highly overall correlated with Unnamed: 11 and 3 other fieldsHigh correlation
Unnamed: 11 is highly overall correlated with Unnamed: 10 and 3 other fieldsHigh correlation
Unnamed: 12 is highly overall correlated with Unnamed: 10 and 4 other fieldsHigh correlation
Unnamed: 13 is highly overall correlated with Unnamed: 10 and 3 other fieldsHigh correlation
Unnamed: 14 is highly overall correlated with Unnamed: 10 and 3 other fieldsHigh correlation
Unnamed: 19 is highly overall correlated with Number and 1 other fieldsHigh correlation
Unnamed: 10 is highly imbalanced (59.1%)Imbalance
Unnamed: 11 is highly imbalanced (59.0%)Imbalance
Unnamed: 12 is highly imbalanced (66.4%)Imbalance
Unnamed: 14 is highly imbalanced (52.2%)Imbalance
Name has 9501 (5.2%) missing valuesMissing
Gender has 171124 (94.5%) missing valuesMissing
JobTitle has 174970 (96.6%) missing valuesMissing
CompanyName has 176976 (97.7%) missing valuesMissing
Email has 121588 (67.1%) missing valuesMissing
Facebook has 176944 (97.7%) missing valuesMissing
Twitter has 178440 (98.5%) missing valuesMissing
Unnamed: 10 has 177169 (97.8%) missing valuesMissing
Unnamed: 11 has 178696 (98.7%) missing valuesMissing
Unnamed: 12 has 179976 (99.4%) missing valuesMissing
Unnamed: 13 has 180811 (99.8%) missing valuesMissing
Unnamed: 14 has 181022 (99.9%) missing valuesMissing
Unnamed: 15 has 181105 (> 99.9%) missing valuesMissing
Unnamed: 16 has 181119 (> 99.9%) missing valuesMissing
Unnamed: 17 has 181128 (> 99.9%) missing valuesMissing
Unnamed: 18 has 181133 (> 99.9%) missing valuesMissing
Unnamed: 19 has 181134 (> 99.9%) missing valuesMissing
Unnamed: 20 has 181135 (> 99.9%) missing valuesMissing
Unnamed: 21 has 181136 (100.0%) missing valuesMissing
Unnamed: 22 has 181135 (> 99.9%) missing valuesMissing
Unnamed: 23 has 181135 (> 99.9%) missing valuesMissing
Unnamed: 24 has 181135 (> 99.9%) missing valuesMissing
Unnamed: 25 has 181136 (100.0%) missing valuesMissing
Unnamed: 26 has 181136 (100.0%) missing valuesMissing
Unnamed: 27 has 181136 (100.0%) missing valuesMissing
Unnamed: 19 is uniformly distributedUniform
Unnamed: 15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 16 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 17 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 18 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 21 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 25 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 26 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 27 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-07-17 23:44:14.697698
Analysis finished2024-07-17 23:44:31.230565
Duration16.53 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Number
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.173685 × 1011
Minimum9.17207 × 1011
Maximum9.17842 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-07-17T23:44:31.368580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.17207 × 1011
5-th percentile9.17207 × 1011
Q19.17208 × 1011
median9.17416 × 1011
Q39.17417 × 1011
95-th percentile9.17842 × 1011
Maximum9.17842 × 1011
Range6.35 × 108
Interquartile range (IQR)2.09 × 108

Descriptive statistics

Standard deviation1.9675842 × 108
Coefficient of variation (CV)0.00021448134
Kurtosis0.70442338
Mean9.173685 × 1011
Median Absolute Deviation (MAD)2.08 × 108
Skewness1.2445991
Sum1.6616846 × 1017
Variance3.8713878 × 1016
MonotonicityNot monotonic
2024-07-17T23:44:31.584876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9.17207 × 101144775
24.7%
9.17208 × 101141612
23.0%
9.17417 × 101135999
19.9%
9.17416 × 101133576
18.5%
9.17842 × 101117804
 
9.8%
9.17659 × 10114926
 
2.7%
9.17658 × 10112444
 
1.3%
ValueCountFrequency (%)
9.17207 × 101144775
24.7%
9.17208 × 101141612
23.0%
9.17416 × 101133576
18.5%
9.17417 × 101135999
19.9%
9.17658 × 10112444
 
1.3%
9.17659 × 10114926
 
2.7%
9.17842 × 101117804
 
9.8%
ValueCountFrequency (%)
9.17842 × 101117804
 
9.8%
9.17659 × 10114926
 
2.7%
9.17658 × 10112444
 
1.3%
9.17417 × 101135999
19.9%
9.17416 × 101133576
18.5%
9.17208 × 101141612
23.0%
9.17207 × 101144775
24.7%

Carrier
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Tata Docomo
181136 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1992496
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTata Docomo
2nd rowTata Docomo
3rd rowTata Docomo
4th rowTata Docomo
5th rowTata Docomo

Common Values

ValueCountFrequency (%)
Tata Docomo 181136
100.0%

Length

2024-07-17T23:44:31.855224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T23:44:32.153398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
tata 181136
50.0%
docomo 181136
50.0%

Most occurring characters

ValueCountFrequency (%)
o 543408
27.3%
a 362272
18.2%
T 181136
 
9.1%
t 181136
 
9.1%
181136
 
9.1%
D 181136
 
9.1%
c 181136
 
9.1%
m 181136
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1992496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 543408
27.3%
a 362272
18.2%
T 181136
 
9.1%
t 181136
 
9.1%
181136
 
9.1%
D 181136
 
9.1%
c 181136
 
9.1%
m 181136
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1992496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 543408
27.3%
a 362272
18.2%
T 181136
 
9.1%
t 181136
 
9.1%
181136
 
9.1%
D 181136
 
9.1%
c 181136
 
9.1%
m 181136
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1992496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 543408
27.3%
a 362272
18.2%
T 181136
 
9.1%
t 181136
 
9.1%
181136
 
9.1%
D 181136
 
9.1%
c 181136
 
9.1%
m 181136
 
9.1%

Name
Text

MISSING 

Distinct130621
Distinct (%)76.1%
Missing9501
Missing (%)5.2%
Memory size1.4 MiB
2024-07-17T23:44:32.706921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length338
Median length87
Mean length11.747435
Min length1

Characters and Unicode

Total characters2016271
Distinct characters187
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120512 ?
Unique (%)70.2%

Sample

1st rowHire Kart Cabs
2nd rowMajith Shareef
3rd rowShivani
4th rowReddy Kailasgeri Photo
5th rowA. E. T Rohith 1
ValueCountFrequency (%)
kumar 5806
 
1.7%
reddy 3529
 
1.0%
sai 2988
 
0.9%
k 2534
 
0.7%
s 2286
 
0.7%
1981
 
0.6%
2 1974
 
0.6%
m 1963
 
0.6%
krishna 1749
 
0.5%
shaik 1714
 
0.5%
Other values (63243) 315676
92.2%
2024-07-17T23:44:33.652136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 323416
16.0%
170626
 
8.5%
i 127860
 
6.3%
h 108396
 
5.4%
n 107991
 
5.4%
r 104222
 
5.2%
e 92014
 
4.6%
u 74505
 
3.7%
d 61154
 
3.0%
s 58893
 
2.9%
Other values (177) 787194
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016271
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 323416
16.0%
170626
 
8.5%
i 127860
 
6.3%
h 108396
 
5.4%
n 107991
 
5.4%
r 104222
 
5.2%
e 92014
 
4.6%
u 74505
 
3.7%
d 61154
 
3.0%
s 58893
 
2.9%
Other values (177) 787194
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016271
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 323416
16.0%
170626
 
8.5%
i 127860
 
6.3%
h 108396
 
5.4%
n 107991
 
5.4%
r 104222
 
5.2%
e 92014
 
4.6%
u 74505
 
3.7%
d 61154
 
3.0%
s 58893
 
2.9%
Other values (177) 787194
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016271
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 323416
16.0%
170626
 
8.5%
i 127860
 
6.3%
h 108396
 
5.4%
n 107991
 
5.4%
r 104222
 
5.2%
e 92014
 
4.6%
u 74505
 
3.7%
d 61154
 
3.0%
s 58893
 
2.9%
Other values (177) 787194
39.0%

Gender
Text

MISSING 

Distinct242
Distinct (%)2.4%
Missing171124
Missing (%)94.5%
Memory size1.4 MiB
2024-07-17T23:44:34.272629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length4
Mean length4.3545745
Min length1

Characters and Unicode

Total characters43598
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique212 ?
Unique (%)2.1%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
4th rowFEMALE
5th rowFEMALE
ValueCountFrequency (%)
male 8220
81.6%
female 1488
 
14.8%
v 15
 
0.1%
2 13
 
0.1%
s 9
 
0.1%
i 7
 
0.1%
m 5
 
< 0.1%
k 5
 
< 0.1%
d 5
 
< 0.1%
c 4
 
< 0.1%
Other values (256) 305
 
3.0%
2024-07-17T23:44:35.185006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 11201
25.7%
M 9729
22.3%
A 9719
22.3%
L 9715
22.3%
F 1489
 
3.4%
241
 
0.6%
a 225
 
0.5%
r 83
 
0.2%
e 73
 
0.2%
n 71
 
0.2%
Other values (71) 1052
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 11201
25.7%
M 9729
22.3%
A 9719
22.3%
L 9715
22.3%
F 1489
 
3.4%
241
 
0.6%
a 225
 
0.5%
r 83
 
0.2%
e 73
 
0.2%
n 71
 
0.2%
Other values (71) 1052
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 11201
25.7%
M 9729
22.3%
A 9719
22.3%
L 9715
22.3%
F 1489
 
3.4%
241
 
0.6%
a 225
 
0.5%
r 83
 
0.2%
e 73
 
0.2%
n 71
 
0.2%
Other values (71) 1052
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 11201
25.7%
M 9729
22.3%
A 9719
22.3%
L 9715
22.3%
F 1489
 
3.4%
241
 
0.6%
a 225
 
0.5%
r 83
 
0.2%
e 73
 
0.2%
n 71
 
0.2%
Other values (71) 1052
 
2.4%

State
Text

Distinct1751
Distinct (%)1.0%
Missing317
Missing (%)0.2%
Memory size1.4 MiB
2024-07-17T23:44:35.751564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length96
Median length17
Mean length15.702011
Min length1

Characters and Unicode

Total characters2839222
Distinct characters116
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1540 ?
Unique (%)0.9%

Sample

1st rowAndhra Pradesh in
2nd rowAndhra Pradesh in
3rd rowAndhra Pradesh
4th rowAndhra Pradesh
5th rowAndhra Pradesh
ValueCountFrequency (%)
pradesh 168471
37.6%
andhra 168469
37.6%
in 94886
21.2%
punjab 6907
 
1.5%
hyderabad 2852
 
0.6%
kotma 422
 
0.1%
vijayawada 225
 
0.1%
visakhapatnam 209
 
< 0.1%
guntur 132
 
< 0.1%
nagar 115
 
< 0.1%
Other values (1954) 4792
 
1.1%
2024-07-17T23:44:36.655342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 358146
12.6%
348591
12.3%
d 344369
12.1%
r 342335
12.1%
h 338408
11.9%
n 272797
9.6%
P 175528
6.2%
e 173079
6.1%
s 169255
6.0%
A 168896
5.9%
Other values (106) 147818
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2839222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 358146
12.6%
348591
12.3%
d 344369
12.1%
r 342335
12.1%
h 338408
11.9%
n 272797
9.6%
P 175528
6.2%
e 173079
6.1%
s 169255
6.0%
A 168896
5.9%
Other values (106) 147818
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2839222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 358146
12.6%
348591
12.3%
d 344369
12.1%
r 342335
12.1%
h 338408
11.9%
n 272797
9.6%
P 175528
6.2%
e 173079
6.1%
s 169255
6.0%
A 168896
5.9%
Other values (106) 147818
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2839222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 358146
12.6%
348591
12.3%
d 344369
12.1%
r 342335
12.1%
h 338408
11.9%
n 272797
9.6%
P 175528
6.2%
e 173079
6.1%
s 169255
6.0%
A 168896
5.9%
Other values (106) 147818
5.2%

JobTitle
Text

MISSING 

Distinct1807
Distinct (%)29.3%
Missing174970
Missing (%)96.6%
Memory size1.4 MiB
2024-07-17T23:44:37.309056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length54
Median length44
Mean length10.221051
Min length1

Characters and Unicode

Total characters63023
Distinct characters124
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1513 ?
Unique (%)24.5%

Sample

1st row Andhra Pradesh Hyderabad
2nd row522101
3rd row India Tirupathi
4th row Andhra Pradesh Hyderabad
5th row in
ValueCountFrequency (%)
in 1936
20.1%
pradesh 1380
14.3%
andhra 951
 
9.9%
hyderabad 791
 
8.2%
india 603
 
6.3%
madhya 426
 
4.4%
vijayawada 127
 
1.3%
bangalore 55
 
0.6%
37
 
0.4%
engineer 36
 
0.4%
Other values (1857) 3300
34.2%
2024-07-17T23:44:38.447823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8171
13.0%
8156
12.9%
d 5527
 
8.8%
n 4605
 
7.3%
r 4384
 
7.0%
i 3735
 
5.9%
e 3348
 
5.3%
h 3329
 
5.3%
s 1993
 
3.2%
y 1554
 
2.5%
Other values (114) 18221
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63023
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8171
13.0%
8156
12.9%
d 5527
 
8.8%
n 4605
 
7.3%
r 4384
 
7.0%
i 3735
 
5.9%
e 3348
 
5.3%
h 3329
 
5.3%
s 1993
 
3.2%
y 1554
 
2.5%
Other values (114) 18221
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63023
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8171
13.0%
8156
12.9%
d 5527
 
8.8%
n 4605
 
7.3%
r 4384
 
7.0%
i 3735
 
5.9%
e 3348
 
5.3%
h 3329
 
5.3%
s 1993
 
3.2%
y 1554
 
2.5%
Other values (114) 18221
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63023
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8171
13.0%
8156
12.9%
d 5527
 
8.8%
n 4605
 
7.3%
r 4384
 
7.0%
i 3735
 
5.9%
e 3348
 
5.3%
h 3329
 
5.3%
s 1993
 
3.2%
y 1554
 
2.5%
Other values (114) 18221
28.9%

CompanyName
Text

MISSING 

Distinct1818
Distinct (%)43.7%
Missing176976
Missing (%)97.7%
Memory size1.4 MiB
2024-07-17T23:44:39.043452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length45
Median length41
Mean length9.7947115
Min length1

Characters and Unicode

Total characters40746
Distinct characters126
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1599 ?
Unique (%)38.4%

Sample

1st row Andhra Pradesh
2nd row Bapatla
3rd row India
4th row Andhra Pradesh
5th row Visakhapatnam
ValueCountFrequency (%)
india 726
 
12.1%
pradesh 587
 
9.8%
andhra 579
 
9.7%
in 385
 
6.4%
hyderabad 229
 
3.8%
40
 
0.7%
ltd 38
 
0.6%
vijayawada 29
 
0.5%
bank 28
 
0.5%
hyd 27
 
0.5%
Other values (1984) 3316
55.4%
2024-07-17T23:44:40.137775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5001
 
12.3%
4337
 
10.6%
d 3003
 
7.4%
n 2985
 
7.3%
r 2664
 
6.5%
i 2474
 
6.1%
e 2187
 
5.4%
h 1715
 
4.2%
s 1409
 
3.5%
t 1121
 
2.8%
Other values (116) 13850
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5001
 
12.3%
4337
 
10.6%
d 3003
 
7.4%
n 2985
 
7.3%
r 2664
 
6.5%
i 2474
 
6.1%
e 2187
 
5.4%
h 1715
 
4.2%
s 1409
 
3.5%
t 1121
 
2.8%
Other values (116) 13850
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5001
 
12.3%
4337
 
10.6%
d 3003
 
7.4%
n 2985
 
7.3%
r 2664
 
6.5%
i 2474
 
6.1%
e 2187
 
5.4%
h 1715
 
4.2%
s 1409
 
3.5%
t 1121
 
2.8%
Other values (116) 13850
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5001
 
12.3%
4337
 
10.6%
d 3003
 
7.4%
n 2985
 
7.3%
r 2664
 
6.5%
i 2474
 
6.1%
e 2187
 
5.4%
h 1715
 
4.2%
s 1409
 
3.5%
t 1121
 
2.8%
Other values (116) 13850
34.0%

Email
Text

MISSING 

Distinct57315
Distinct (%)96.3%
Missing121588
Missing (%)67.1%
Memory size1.4 MiB
2024-07-17T23:44:40.845257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length100
Median length50
Mean length22.850373
Min length1

Characters and Unicode

Total characters1360694
Distinct characters114
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57188 ?
Unique (%)96.0%

Sample

1st rowsanjeeva.g@hirekart.com
2nd rowabdulmoid7866@gmail.com
3rd rowsruthidolls16@gmail.com
4th rowvindiv.singla@gmail.com
5th rowgauravj43@gmail.com
ValueCountFrequency (%)
in 1939
 
3.2%
pradesh 123
 
0.2%
andhra 122
 
0.2%
hyderabad 27
 
< 0.1%
india 13
 
< 0.1%
abc@gmail.com 12
 
< 0.1%
student 8
 
< 0.1%
telangana 8
 
< 0.1%
8
 
< 0.1%
maharashtra 7
 
< 0.1%
Other values (57471) 57705
96.2%
2024-07-17T23:44:42.172590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 181831
 
13.4%
m 140703
 
10.3%
i 106217
 
7.8%
l 77009
 
5.7%
o 75910
 
5.6%
. 69915
 
5.1%
g 65682
 
4.8%
c 63883
 
4.7%
@ 57054
 
4.2%
r 48517
 
3.6%
Other values (104) 473973
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1360694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 181831
 
13.4%
m 140703
 
10.3%
i 106217
 
7.8%
l 77009
 
5.7%
o 75910
 
5.6%
. 69915
 
5.1%
g 65682
 
4.8%
c 63883
 
4.7%
@ 57054
 
4.2%
r 48517
 
3.6%
Other values (104) 473973
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1360694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 181831
 
13.4%
m 140703
 
10.3%
i 106217
 
7.8%
l 77009
 
5.7%
o 75910
 
5.6%
. 69915
 
5.1%
g 65682
 
4.8%
c 63883
 
4.7%
@ 57054
 
4.2%
r 48517
 
3.6%
Other values (104) 473973
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1360694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 181831
 
13.4%
m 140703
 
10.3%
i 106217
 
7.8%
l 77009
 
5.7%
o 75910
 
5.6%
. 69915
 
5.1%
g 65682
 
4.8%
c 63883
 
4.7%
@ 57054
 
4.2%
r 48517
 
3.6%
Other values (104) 473973
34.8%

Facebook
Text

MISSING 

Distinct1598
Distinct (%)38.1%
Missing176944
Missing (%)97.7%
Memory size1.4 MiB
2024-07-17T23:44:42.818869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length93
Median length8
Mean length9.6331107
Min length1

Characters and Unicode

Total characters40382
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1115 ?
Unique (%)26.6%

Sample

1st row1.00E+14
2nd row1.00E+14
3rd rowStundent
4th row1.62E+15
5th row1.00E+14
ValueCountFrequency (%)
1.00e+14 1190
 
26.4%
in 173
 
3.8%
1.02e+16 16
 
0.4%
1.90e+15 14
 
0.3%
1.80e+15 13
 
0.3%
1.78e+15 12
 
0.3%
12
 
0.3%
1.97e+15 11
 
0.2%
2.12e+15 11
 
0.2%
ltd 10
 
0.2%
Other values (1775) 3052
67.6%
2024-07-17T23:44:43.772069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5904
14.6%
. 3666
 
9.1%
4 3224
 
8.0%
E 3195
 
7.9%
+ 3138
 
7.8%
0 3057
 
7.6%
a 1504
 
3.7%
5 1297
 
3.2%
i 1058
 
2.6%
2 986
 
2.4%
Other values (105) 13353
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5904
14.6%
. 3666
 
9.1%
4 3224
 
8.0%
E 3195
 
7.9%
+ 3138
 
7.8%
0 3057
 
7.6%
a 1504
 
3.7%
5 1297
 
3.2%
i 1058
 
2.6%
2 986
 
2.4%
Other values (105) 13353
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5904
14.6%
. 3666
 
9.1%
4 3224
 
8.0%
E 3195
 
7.9%
+ 3138
 
7.8%
0 3057
 
7.6%
a 1504
 
3.7%
5 1297
 
3.2%
i 1058
 
2.6%
2 986
 
2.4%
Other values (105) 13353
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5904
14.6%
. 3666
 
9.1%
4 3224
 
8.0%
E 3195
 
7.9%
+ 3138
 
7.8%
0 3057
 
7.6%
a 1504
 
3.7%
5 1297
 
3.2%
i 1058
 
2.6%
2 986
 
2.4%
Other values (105) 13353
33.1%

Twitter
Text

MISSING 

Distinct2148
Distinct (%)79.7%
Missing178440
Missing (%)98.5%
Memory size1.4 MiB
2024-07-17T23:44:44.360385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length50
Median length38
Mean length18.00816
Min length2

Characters and Unicode

Total characters48550
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2050 ?
Unique (%)76.0%

Sample

1st rowpraveengupta.mankala@gmail.com
2nd rowvjfriends4ever@gmail.com
3rd rowkandularani@gmail.com
4th row7207000642.in@gmail.com
5th row1.63E+15
ValueCountFrequency (%)
1.00e+14 388
 
14.1%
in 13
 
0.5%
1.02e+16 9
 
0.3%
2.00e+15 6
 
0.2%
1.61e+15 5
 
0.2%
hyderabad 5
 
0.2%
1.63e+15 5
 
0.2%
india 5
 
0.2%
1.64e+15 4
 
0.1%
1.70e+15 4
 
0.1%
Other values (2185) 2308
83.9%
2024-07-17T23:44:45.516220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5358
 
11.0%
m 4048
 
8.3%
i 3141
 
6.5%
. 2980
 
6.1%
o 2441
 
5.0%
l 2238
 
4.6%
1 2060
 
4.2%
c 1938
 
4.0%
g 1847
 
3.8%
@ 1678
 
3.5%
Other values (65) 20821
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5358
 
11.0%
m 4048
 
8.3%
i 3141
 
6.5%
. 2980
 
6.1%
o 2441
 
5.0%
l 2238
 
4.6%
1 2060
 
4.2%
c 1938
 
4.0%
g 1847
 
3.8%
@ 1678
 
3.5%
Other values (65) 20821
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5358
 
11.0%
m 4048
 
8.3%
i 3141
 
6.5%
. 2980
 
6.1%
o 2441
 
5.0%
l 2238
 
4.6%
1 2060
 
4.2%
c 1938
 
4.0%
g 1847
 
3.8%
@ 1678
 
3.5%
Other values (65) 20821
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5358
 
11.0%
m 4048
 
8.3%
i 3141
 
6.5%
. 2980
 
6.1%
o 2441
 
5.0%
l 2238
 
4.6%
1 2060
 
4.2%
c 1938
 
4.0%
g 1847
 
3.8%
@ 1678
 
3.5%
Other values (65) 20821
42.9%

Unnamed: 10
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct24
Distinct (%)0.6%
Missing177169
Missing (%)97.8%
Memory size1.4 MiB
0
1685 
0.9
1293 
user
645 
verified
295 
 
20
Other values (19)
 
29

Length

Max length27
Median length22
Mean length2.7229645
Min length1

Characters and Unicode

Total characters10802
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.4%

Sample

1st row0
2nd row0.9
3rd row0.9
4th rowuser
5th row0.9

Common Values

ValueCountFrequency (%)
0 1685
 
0.9%
0.9 1293
 
0.7%
user 645
 
0.4%
verified 295
 
0.2%
20
 
< 0.1%
College 7
 
< 0.1%
premium 4
 
< 0.1%
Information technology 2
 
< 0.1%
0.31984797 1
 
< 0.1%
Police 1
 
< 0.1%
Other values (14) 14
 
< 0.1%
(Missing) 177169
97.8%

Length

2024-07-17T23:44:45.931012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 1685
42.7%
0.9 1293
32.7%
user 645
 
16.3%
verified 295
 
7.5%
college 7
 
0.2%
premium 4
 
0.1%
information 2
 
0.1%
technology 2
 
0.1%
gym 1
 
< 0.1%
4.15e+14 1
 
< 0.1%
Other values (14) 14
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 2986
27.6%
. 1305
12.1%
9 1298
12.0%
e 1257
11.6%
r 949
 
8.8%
u 651
 
6.0%
s 648
 
6.0%
i 602
 
5.6%
v 297
 
2.7%
f 297
 
2.7%
Other values (31) 512
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10802
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2986
27.6%
. 1305
12.1%
9 1298
12.0%
e 1257
11.6%
r 949
 
8.8%
u 651
 
6.0%
s 648
 
6.0%
i 602
 
5.6%
v 297
 
2.7%
f 297
 
2.7%
Other values (31) 512
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10802
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2986
27.6%
. 1305
12.1%
9 1298
12.0%
e 1257
11.6%
r 949
 
8.8%
u 651
 
6.0%
s 648
 
6.0%
i 602
 
5.6%
v 297
 
2.7%
f 297
 
2.7%
Other values (31) 512
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10802
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2986
27.6%
. 1305
12.1%
9 1298
12.0%
e 1257
11.6%
r 949
 
8.8%
u 651
 
6.0%
s 648
 
6.0%
i 602
 
5.6%
v 297
 
2.7%
f 297
 
2.7%
Other values (31) 512
 
4.7%

Unnamed: 11
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct14
Distinct (%)0.6%
Missing178696
Missing (%)98.7%
Memory size1.4 MiB
0
1295 
0.9
838 
user
212 
verified
 
70
 
14
Other values (9)
 
11

Length

Max length21
Median length1
Mean length2.1795082
Min length1

Characters and Unicode

Total characters5318
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.3%

Sample

1st row0
2nd row0
3rd row0.9
4th row0
5th rowuser

Common Values

ValueCountFrequency (%)
0 1295
 
0.7%
0.9 838
 
0.5%
user 212
 
0.1%
verified 70
 
< 0.1%
14
 
< 0.1%
premium 2
 
< 0.1%
College 2
 
< 0.1%
Sirakrim 1
 
< 0.1%
0.31564713 1
 
< 0.1%
10 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
(Missing) 178696
98.7%

Length

2024-07-17T23:44:46.353795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 1295
53.4%
0.9 838
34.5%
user 212
 
8.7%
verified 70
 
2.9%
premium 2
 
0.1%
college 2
 
0.1%
sirakrim 1
 
< 0.1%
0.31564713 1
 
< 0.1%
10 1
 
< 0.1%
sathya88887@gmail.com 1
 
< 0.1%
Other values (3) 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2136
40.2%
. 841
 
15.8%
9 839
 
15.8%
e 359
 
6.8%
r 286
 
5.4%
u 214
 
4.0%
s 213
 
4.0%
i 146
 
2.7%
f 70
 
1.3%
d 70
 
1.3%
Other values (25) 144
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5318
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2136
40.2%
. 841
 
15.8%
9 839
 
15.8%
e 359
 
6.8%
r 286
 
5.4%
u 214
 
4.0%
s 213
 
4.0%
i 146
 
2.7%
f 70
 
1.3%
d 70
 
1.3%
Other values (25) 144
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5318
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2136
40.2%
. 841
 
15.8%
9 839
 
15.8%
e 359
 
6.8%
r 286
 
5.4%
u 214
 
4.0%
s 213
 
4.0%
i 146
 
2.7%
f 70
 
1.3%
d 70
 
1.3%
Other values (25) 144
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5318
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2136
40.2%
. 841
 
15.8%
9 839
 
15.8%
e 359
 
6.8%
r 286
 
5.4%
u 214
 
4.0%
s 213
 
4.0%
i 146
 
2.7%
f 70
 
1.3%
d 70
 
1.3%
Other values (25) 144
 
2.7%

Unnamed: 12
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct13
Distinct (%)1.1%
Missing179976
Missing (%)99.4%
Memory size1.4 MiB
0
840 
0.9
220 
user
 
56
verified
 
33
 
3
Other values (8)
 
8

Length

Max length22
Median length1
Mean length1.8094828
Min length1

Characters and Unicode

Total characters2099
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.7%

Sample

1st row0
2nd row0.9
3rd row0.9
4th row0.9
5th row0

Common Values

ValueCountFrequency (%)
0 840
 
0.5%
0.9 220
 
0.1%
user 56
 
< 0.1%
verified 33
 
< 0.1%
3
 
< 0.1%
apandey750@gmail.com 1
 
< 0.1%
Pharmacy 1
 
< 0.1%
Furniture store 1
 
< 0.1%
College 1
 
< 0.1%
sathya.com 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 179976
99.4%

Length

2024-07-17T23:44:46.820030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 840
72.4%
0.9 220
 
18.9%
user 56
 
4.8%
verified 33
 
2.8%
apandey750@gmail.com 1
 
0.1%
pharmacy 1
 
0.1%
furniture 1
 
0.1%
store 1
 
0.1%
college 1
 
0.1%
sathya.com 1
 
0.1%
Other values (6) 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1061
50.5%
. 222
 
10.6%
9 220
 
10.5%
e 129
 
6.1%
r 94
 
4.5%
i 71
 
3.4%
s 60
 
2.9%
u 59
 
2.8%
d 35
 
1.7%
v 34
 
1.6%
Other values (23) 114
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2099
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1061
50.5%
. 222
 
10.6%
9 220
 
10.5%
e 129
 
6.1%
r 94
 
4.5%
i 71
 
3.4%
s 60
 
2.9%
u 59
 
2.8%
d 35
 
1.7%
v 34
 
1.6%
Other values (23) 114
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2099
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1061
50.5%
. 222
 
10.6%
9 220
 
10.5%
e 129
 
6.1%
r 94
 
4.5%
i 71
 
3.4%
s 60
 
2.9%
u 59
 
2.8%
d 35
 
1.7%
v 34
 
1.6%
Other values (23) 114
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2099
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1061
50.5%
. 222
 
10.6%
9 220
 
10.5%
e 129
 
6.1%
r 94
 
4.5%
i 71
 
3.4%
s 60
 
2.9%
u 59
 
2.8%
d 35
 
1.7%
v 34
 
1.6%
Other values (23) 114
 
5.4%

Unnamed: 13
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)1.5%
Missing180811
Missing (%)99.8%
Memory size1.4 MiB
0
220 
0.9
82 
user
 
14
 
6
verified
 
3

Length

Max length8
Median length1
Mean length1.6984615
Min length1

Characters and Unicode

Total characters552
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0.9

Common Values

ValueCountFrequency (%)
0 220
 
0.1%
0.9 82
 
< 0.1%
user 14
 
< 0.1%
6
 
< 0.1%
verified 3
 
< 0.1%
(Missing) 180811
99.8%

Length

2024-07-17T23:44:47.201308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T23:44:47.637905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 220
69.0%
0.9 82
 
25.7%
user 14
 
4.4%
verified 3
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 302
54.7%
. 82
 
14.9%
9 82
 
14.9%
e 20
 
3.6%
r 17
 
3.1%
u 14
 
2.5%
s 14
 
2.5%
6
 
1.1%
i 6
 
1.1%
v 3
 
0.5%
Other values (2) 6
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 302
54.7%
. 82
 
14.9%
9 82
 
14.9%
e 20
 
3.6%
r 17
 
3.1%
u 14
 
2.5%
s 14
 
2.5%
6
 
1.1%
i 6
 
1.1%
v 3
 
0.5%
Other values (2) 6
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 302
54.7%
. 82
 
14.9%
9 82
 
14.9%
e 20
 
3.6%
r 17
 
3.1%
u 14
 
2.5%
s 14
 
2.5%
6
 
1.1%
i 6
 
1.1%
v 3
 
0.5%
Other values (2) 6
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 302
54.7%
. 82
 
14.9%
9 82
 
14.9%
e 20
 
3.6%
r 17
 
3.1%
u 14
 
2.5%
s 14
 
2.5%
6
 
1.1%
i 6
 
1.1%
v 3
 
0.5%
Other values (2) 6
 
1.1%

Unnamed: 14
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct8
Distinct (%)7.0%
Missing181022
Missing (%)99.9%
Memory size1.4 MiB
0
82 
0.9
13 
user
11 
verified
 
4
Andhra Pradesh in
 
1
Other values (3)
 
3

Length

Max length17
Median length1
Mean length2.0964912
Min length1

Characters and Unicode

Total characters239
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)3.5%

Sample

1st row0
2nd row0.9
3rd rowAndhra Pradesh in
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 82
 
< 0.1%
0.9 13
 
< 0.1%
user 11
 
< 0.1%
verified 4
 
< 0.1%
Andhra Pradesh in 1
 
< 0.1%
9.27E+14 1
 
< 0.1%
College 1
 
< 0.1%
0.30263454 1
 
< 0.1%
(Missing) 181022
99.9%

Length

2024-07-17T23:44:48.000015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T23:44:48.288663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 82
70.7%
0.9 13
 
11.2%
user 11
 
9.5%
verified 4
 
3.4%
andhra 1
 
0.9%
pradesh 1
 
0.9%
in 1
 
0.9%
9.27e+14 1
 
0.9%
college 1
 
0.9%
0.30263454 1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 97
40.6%
e 22
 
9.2%
r 17
 
7.1%
. 15
 
6.3%
9 14
 
5.9%
s 12
 
5.0%
u 11
 
4.6%
i 9
 
3.8%
d 6
 
2.5%
v 4
 
1.7%
Other values (20) 32
 
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 97
40.6%
e 22
 
9.2%
r 17
 
7.1%
. 15
 
6.3%
9 14
 
5.9%
s 12
 
5.0%
u 11
 
4.6%
i 9
 
3.8%
d 6
 
2.5%
v 4
 
1.7%
Other values (20) 32
 
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 97
40.6%
e 22
 
9.2%
r 17
 
7.1%
. 15
 
6.3%
9 14
 
5.9%
s 12
 
5.0%
u 11
 
4.6%
i 9
 
3.8%
d 6
 
2.5%
v 4
 
1.7%
Other values (20) 32
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 97
40.6%
e 22
 
9.2%
r 17
 
7.1%
. 15
 
6.3%
9 14
 
5.9%
s 12
 
5.0%
u 11
 
4.6%
i 9
 
3.8%
d 6
 
2.5%
v 4
 
1.7%
Other values (20) 32
 
13.4%

Unnamed: 15
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing181105
Missing (%)> 99.9%
Memory size1.4 MiB

Unnamed: 16
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing181119
Missing (%)> 99.9%
Memory size1.4 MiB

Unnamed: 17
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing181128
Missing (%)> 99.9%
Memory size1.4 MiB

Unnamed: 18
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing181133
Missing (%)> 99.9%
Memory size1.4 MiB

Unnamed: 19
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing181134
Missing (%)> 99.9%
Memory size1.4 MiB
0.0
0.9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.0
2nd row0.9

Common Values

ValueCountFrequency (%)
0.0 1
 
< 0.1%
0.9 1
 
< 0.1%
(Missing) 181134
> 99.9%

Length

2024-07-17T23:44:48.573778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T23:44:48.831410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
50.0%
0.9 1
50.0%

Most occurring characters

ValueCountFrequency (%)
0 3
50.0%
. 2
33.3%
9 1
 
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3
50.0%
. 2
33.3%
9 1
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3
50.0%
. 2
33.3%
9 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3
50.0%
. 2
33.3%
9 1
 
16.7%

Unnamed: 20
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing181135
Missing (%)> 99.9%
Memory size1.4 MiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0

Common Values

ValueCountFrequency (%)
0.0 1
 
< 0.1%
(Missing) 181135
> 99.9%

Length

2024-07-17T23:44:49.035792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T23:44:49.262096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Unnamed: 21
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing181136
Missing (%)100.0%
Memory size1.4 MiB

Unnamed: 22
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing181135
Missing (%)> 99.9%
Memory size1.4 MiB
2024-07-17T23:44:49.408401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowuser
ValueCountFrequency (%)
user 1
100.0%
2024-07-17T23:44:49.867631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 1
25.0%
s 1
25.0%
e 1
25.0%
r 1
25.0%

Unnamed: 23
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing181135
Missing (%)> 99.9%
Memory size1.4 MiB
0.9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.9

Common Values

ValueCountFrequency (%)
0.9 1
 
< 0.1%
(Missing) 181135
> 99.9%

Length

2024-07-17T23:44:50.349258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T23:44:50.606377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.9 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1
33.3%
. 1
33.3%
9 1
33.3%

Unnamed: 24
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing181135
Missing (%)> 99.9%
Memory size1.4 MiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0

Common Values

ValueCountFrequency (%)
0.0 1
 
< 0.1%
(Missing) 181135
> 99.9%

Length

2024-07-17T23:44:50.787285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T23:44:51.011265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Unnamed: 25
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing181136
Missing (%)100.0%
Memory size1.4 MiB

Unnamed: 26
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing181136
Missing (%)100.0%
Memory size1.4 MiB

Unnamed: 27
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing181136
Missing (%)100.0%
Memory size1.4 MiB

Interactions

2024-07-17T23:44:25.064578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-07-17T23:44:51.171228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
NumberUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 19
Number1.0000.0770.0240.0320.0000.0001.000
Unnamed: 100.0771.0000.5750.8210.8530.5600.000
Unnamed: 110.0240.5751.0000.8360.6400.654NaN
Unnamed: 120.0320.8210.8361.0000.7610.8071.000
Unnamed: 130.0000.8530.6400.7611.0000.7560.000
Unnamed: 140.0000.5600.6540.8070.7561.000NaN
Unnamed: 191.0000.000NaN1.0000.000NaN1.000

Missing values

2024-07-17T23:44:25.981067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-17T23:44:28.016760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-17T23:44:30.389992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NumberCarrierNameGenderStateJobTitleCompanyNameEmailFacebookTwitterUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20Unnamed: 21Unnamed: 22Unnamed: 23Unnamed: 24Unnamed: 25Unnamed: 26Unnamed: 27
09.172070e+11Tata DocomoHire Kart CabsNaNAndhra Pradesh inNaNNaNsanjeeva.g@hirekart.comNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
19.172070e+11Tata DocomoMajith ShareefNaNAndhra Pradesh inNaNNaNabdulmoid7866@gmail.comNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
29.172070e+11Tata DocomoShivaniNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39.172070e+11Tata DocomoReddy Kailasgeri PhotoNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
49.172070e+11Tata DocomoA. E. T Rohith 1NaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
59.172070e+11Tata DocomoNaga BhushanaNaNAndhra Pradesh inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
69.172070e+11Tata DocomoT PNaNAndhra Pradesh inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
79.172070e+11Tata DocomoSri SruthiNaNAndhra Pradesh inNaNNaNsruthidolls16@gmail.comNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
89.172070e+11Tata DocomoHariNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99.172070e+11Tata DocomoSai Pal Gun KtsNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
NumberCarrierNameGenderStateJobTitleCompanyNameEmailFacebookTwitterUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20Unnamed: 21Unnamed: 22Unnamed: 23Unnamed: 24Unnamed: 25Unnamed: 26Unnamed: 27
1811269.178420e+11Tata DocomoNaveen NNaNAndhra Pradesh inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1811279.178420e+11Tata DocomoSatish Love U RaNaNAndhra Pradesh inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1811289.178420e+11Tata DocomoMohd Omer ShareefNaNAndhra Pradesh inNaNNaNOmershareef@gmail.comNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1811299.178420e+11Tata DocomoShabazNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1811309.178420e+11Tata DocomoPadma PadmavathiNaNAndhra Pradesh inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1811319.178420e+11Tata DocomoKhaleel MohammedNaNAndhra Pradesh inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1811329.178420e+11Tata DocomoVisalakshiNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1811339.178420e+11Tata DocomoShahnaaz ShaazNaNAndhra Pradesh inNaNNaNshahnaazshaik44@gmail.comNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1811349.178420e+11Tata DocomoRavali ThateepalliNaNAndhra Pradesh inNaNNaNravali3493@gmail.comNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1811359.178420e+11Tata DocomoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

NumberCarrierNameGenderStateJobTitleCompanyNameEmailFacebookTwitterUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 19Unnamed: 20Unnamed: 22Unnamed: 23Unnamed: 24# duplicates
52959.176590e+11Tata DocomoNaNNaNPunjabNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1466
27869.172080e+11Tata DocomoNaNNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1203
51769.176580e+11Tata DocomoNaNNaNPunjabNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1154
51549.174170e+11Tata DocomoNaNNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN919
13969.172070e+11Tata DocomoNaNNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN912
13979.172070e+11Tata DocomoNaNNaNAndhra Pradesh inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN621
27879.172080e+11Tata DocomoNaNNaNAndhra Pradesh inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN540
39399.174160e+11Tata DocomoNaNNaNAndhra Pradesh inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN521
39389.174160e+11Tata DocomoNaNNaNAndhra PradeshNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN493
51559.174170e+11Tata DocomoNaNNaNAndhra Pradesh inNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN493